Task-oriented conversational modeling with unstructured knowledge access, as
track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to
build a system to generate response given dialogue history and knowledge
access. This challenge can be separated into three subtasks, (1)
knowledge-seeking turn detection, (2) knowledge selection, and (3)
knowledge-grounded response generation. We use pre-trained language models,
ELECTRA and RoBERTa, as our base encoder for different subtasks. For subtask 1
and 2, the coarse-grained information like domain and entity are used to
enhance knowledge usage. For subtask 3, we use a latent variable to encode
dialog history and selected knowledge better and generate responses combined
with copy mechanism. Meanwhile, some useful post-processing strategies are
performed on the model's final output to make further knowledge usage in the
generation task. As shown in released evaluation results, our proposed system
ranks second under objective metrics and ranks fourth under human metrics.

本文描述利用预训练语言模型及粗层次知识信息进行基于任务的对话建模，实现对话历史和知识访问的响应生成，并在模型最终输出的基础上进行后处理策略以进一步提升知识应用，在客观指标下获得第二名，在人类指标下获得第四名的实验结果。